The safety valve breaking in a lithium-ion battery emits a distinctive click-hiss sound, similar to cracking open a soda, offering a potential early warning of failure.
Researchers at NIST utilized machine learning to distinguish this sound from common background noise, achieving a 94 percent detection rate for overheating batteries.
Battery fires pose significant risks, generating flames up to 1,100°C very quickly, making early detection critical to avoid disasters in confined spaces.
The challenge in detecting battery failures is distinguishing the unique sounds from various distractions, a problem NIST tackled successfully with advanced audio analysis techniques.
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